Top-K structural diversity search in large networks

Huang, Xin; Cheng, Hong; Li, Ronghua; Qin, Lu; Yu, Jeffrey Xu

doi:10.14778/2536258.2536272

Cited by 26 publications

(36 citation statements)

References 20 publications

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“…We also assume m ∈ Ω(n) for convenience. Similar assumptions can be found in existing works such as [46,59].…”

Section: Representative Search In Large Social Networksupporting

confidence: 52%

“…The most related works about ranking top-k vertices are [46], which studies top-k structure diversity search; [101], which discusses top-k aggregation query; and [53], which focuses on top-k closeness centrality search. To illustrate the difference between our work and the others, we use a star-like graph such that each leaf of the star consists of a clique with 4 nodes as an example (see Figure 2.1).…”

Section: Discussionmentioning

confidence: 99%

“…If we query top-1 structural diversity node in Figure 2.1, [46] will choose the star center S coloured in blue. In [101], aggregate values are computed for vertices in a vertex-weighted graph and vertices are ranked solely based on the aggregate values.…”

Section: Discussionmentioning

confidence: 99%

“…The problem of finding representative vertices based on different ranking measures has drawn a great deal of attention. For example, there are works about finding top-k vertices based on aggregate values [101], structural diversities [46], or closeness centralities [53]. Nevertheless, these measures have limitations in supporting real applications that involve large dynamic graphs.…”

Section: Event Representative Searchmentioning

confidence: 99%

“…The problem of finding top-k vertices based on different ranking measures has been studied recently; for example, finding top-k vertices based on aggregate values [101], based on structural diversities [46], or based on closeness centralities [53]. However, due to different ranking measures, these…”

Section: Literature Reviewmentioning

confidence: 99%

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Multi-faceted analytics of social events: Identification, representation and monitoring

Li¹

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The advances of social media technologies are significantly changing people's way of connecting with each other, sharing information, and understanding the world. Conventionally, stories and reports from newspapers and magazines provide a primary data source. Nowadays, the emerging social media sharing websites (e.g., Facebook, YouTube, Twitter, Flickr) enable Web users to share easily their personal life and express their opinions at any time. With a rapidly increasing number of users in social media services, the mainstream information source is moving to the Web. There has been a dramatically fast growth of social media data in both volume and topic diversity, which makes social event analysis possible. We consider all the user generated contents as social media data, such as tweets on Twitter, posts on Facebook, images on Flickr, etc. For simplicity of explanation, in this thesis we note all of them as posts.The tremendous boost of online social media data enables identification of popular events before the mainstream media, which can be potentially used in real-time notification of emerging events.Notifying mobile users of fires and crimes happening nearby is one example. It may also offer opportunities to governments for timely responses to urgent events at various stages. Furthermore, events are often not isolated. New events may keep coming up successively as a consequence of an initial event, whereas the topic focus on the subsequent events may shift due to the event evolvement.Modelling the implicit relationships among events could help better understand the event evolution process and predict future events. After an event is identified, it is unnecessary to present all related posts about the event to the public. Website users usually prefer to view the event with several representative posts rather than all of them. A cover with selected posts would help users better understand and recall the event. This leads to the problem of how to efficiently and effectively find representatives for events.The first task in this thesis is identifying social events. Social event identification utilizes social media data to detect events and plays a critical role in many applications. For example, early warnings of impending natural disasters or disease are necessary for the safety and security of populations within the affected areas. Therefore, under a variety of situations, there is a demand to derive an effective approach to identify events and report to the public. In this task, we propose a novel approach for identifying events from social media data according to their spatio-temporal context and textual information. To better capture the extensibility of social events in both spatial and temporal space, spatio-temporal expansions are employed.The second task is modelling relationships among events. We observe that different events may tell different stories under the same topic and narrate the development of a topic together. In other words, a growing topic is composed of a series of relevant events,...

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“…We also assume m ∈ Ω(n) for convenience. Similar assumptions can be found in existing works such as [46,59].…”

Section: Representative Search In Large Social Networksupporting

confidence: 52%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Event Representative Searchmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Multi-faceted analytics of social events: Identification, representation and monitoring

Li¹

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Parameter-Free Structural Diversity Search

Huang

Zhu

et al. 2019

Web Information Systems Engineering – WISE 2019

Self Cite

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The problem of structural diversity search is to find the top-k vertices with the largest structural diversity in a graph. However, when identifying distinct social contexts, existing structural diversity models (e.g., t-sized component, t-core, and t-brace) are sensitive to an input parameter of t. To address this drawback, we propose a parameter-free structural diversity model. Specifically, we propose a novel notation of discriminative core, which automatically models various kinds of social contexts without parameter t. Leveraging on discriminative cores and h-index, the structural diversity score for a vertex is calculated. We study the problem of parameterfree structural diversity search in this paper. An efficient top-k search algorithm with a well-designed upper bound for pruning is proposed. Extensive experiment results demonstrate the parameter sensitivity of existing t-core based model and verify the superiority of our methods. ACM Reference Format:

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Community Search over Big Graphs

Huang

Lakshmanan

2019

Synthesis Lectures on Data Management

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Top-K structural diversity search in large networks

Cited by 26 publications

References 20 publications

Multi-faceted analytics of social events: Identification, representation and monitoring

Multi-faceted analytics of social events: Identification, representation and monitoring

Parameter-Free Structural Diversity Search

Community Search over Big Graphs

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